Temporal Queries Research Articles

Enhanced calendar support for temporal datacube queries

AbstractA significant share of our data are timeseries. Time can occur as the single dimension, or further (such as spatial) dimensions can be present as well, leading to multidimensional data such as 3D x/y/t satellite image timeseries or 4D x/y/z/t atmospheric simulations like weather forecasts. However, no user would like to count “seconds since epoch,” rather we all prefer calendar notation. Libraries, SQL, etc. support calendar arithmetics, yet more advanced functionality is needed. Instead of demoting advanced calendar functionality to low‐level coding, it seems desirable to have semantic support for calendars at high‐level, declarative query level. We present a conceptual framework for temporal modeling which allows the expression of a wide range of temporal queries. It smoothly enhances existing calendar handling by adding two new data and query description parameters, period of validity, and granularity. While our work is embedded in spatio‐temporal geo services, it can be incorporated in a non‐breaking manner in any environment needing calendar addressing. The concepts are implemented in an Array DBMS operational on several Petabytes of Earth datacubes.

MobileInst: Video Instance Segmentation on the Mobile

Video instance segmentation on mobile devices is an important yet very challenging edge AI problem. It mainly suffers from (1) heavy computation and memory costs for frame-by-frame pixel-level instance perception and (2) complicated heuristics for tracking objects. To address these issues, we present MobileInst, a lightweight and mobile-friendly framework for video instance segmentation on mobile devices. Firstly, MobileInst adopts a mobile vision transformer to extract multi-level semantic features and presents an efficient query-based dual-transformer instance decoder for mask kernels and a semantic-enhanced mask decoder to generate instance segmentation per frame. Secondly, MobileInst exploits simple yet effective kernel reuse and kernel association to track objects for video instance segmentation. Further, we propose temporal query passing to enhance the tracking ability for kernels. We conduct experiments on COCO and YouTube-VIS datasets to demonstrate the superiority of MobileInst and evaluate the inference latency on one single CPU core of the Snapdragon 778G Mobile Platform, without other methods of acceleration. On the COCO dataset, MobileInst achieves 31.2 mask AP and 433 ms on the mobile CPU, which reduces the latency by 50% compared to the previous SOTA. For video instance segmentation, MobileInst achieves 35.0 AP and 30.1 AP on YouTube-VIS 2019 & 2021.

AeonG: An Efficient Built-in Temporal Support in Graph Databases

Real-world graphs are often dynamic and evolve over time. It is crucial for storing and querying a graph's evolution in graph databases. However, existing works either suffer from high storage overhead or lack efficient temporal query support, or both. In this paper, we propose AeonG, a new graph database with built-in temporal support. AeonG is based on a novel temporal graph model. To fit this model, we design a storage engine and a query engine. Our storage engine is hybrid, with one current storage to manage the most recent versions of graph objects, and another historical storage to manage the previous versions of graph objects. This separation makes the performance degradation of querying the most recent graph object versions as slight as possible. To reduce the historical storage overhead, we propose a novel anchor+delta strategy, in which we periodically create a complete version (namely anchor) of a graph object, and maintain every change (namely delta) between two adjacent anchors of the same object. To boost temporal query processing, we propose an anchor-based version retrieval technique in the query engine to skip unnecessary historical version traversals. Extensive experiments are conducted on both real and synthetic datasets. The results show that AeonG achieves up to 5.73× lower storage consumption and 2.57× lower temporal query latency against state-of-the-art approaches, while introducing only 9.74% performance degradation for supporting temporal features.

Enhancing Data Engineering Frameworks for Scalable Real-Time Marketing Solutions

This report discusses improvements to the data engineering frameworks for cost-efficient real-time marketing environment, with the applications in mobile advertising and smart city analytics. The proposed framework combines the processing of time-series data with map-reduce (M-R) systems in order to compensate for inefficiencies in the execution of temporal queries and the processing of real-time data streams. It enables effective behavioral targeting in mobile marketing and helps integrate various domains’ real-time data for smart city utilization. Specific examples that illustrate further refinements in the algorithms are showcased through case study and experimental evidence where gains in throughput, data reliability, and system effectiveness are quantified in real-time Marketing and Smart City applications.

τSQWRL: A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

Temporal ontologies allow to represent not only concepts, their properties, and their relationships, but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view. They are widely used to formally represent temporal data semantics in several applications belonging to different fields (e.g., Semantic Web, expert systems, knowledge bases, big data, and artificial intelligence). They facilitate temporal knowledge representation and discovery, with the support of temporal data querying and reasoning. However, there is no standard or consensual temporal ontology query language. In a previous work, we have proposed an approach named τJOWL (temporal OWL 2 from temporal JSON, where OWL 2 stands for “OWL 2 Web Ontology Language” and JSON stands for “JavaScript Object Notation”). τJOWL allows (1) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (2) to manage its incremental maintenance accommodating their evolution, in a temporal and multi-schema-version environment. In this paper, we propose a temporal ontology query language for rJOWL, named rSQWRL (temporal SQWRL), designed as a temporal extension of the ontology query language-Semantic Query-enhanced Web Rule Language (SQWRL). The new language has been inspired by the features of the consensual temporal query language TSQL2 (Temporal SQL2), well known in the temporal (relational) database community. The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any (complex) temporal query on time-varying ontologies generated from time-varying big data. Some examples, in the Internet of Healthcare Things (IoHT) domain, are provided to motivate and illustrate our proposal.

Data-Aware Declarative Process Mining with SAT

Process Mining is a family of techniques for analyzing business process execution data recorded in event logs. Process models can be obtained as output of automated process discovery techniques or can be used as input of techniques for conformance checking or model enhancement. In Declarative Process Mining, process models are represented as sets of temporal constraints (instead of procedural descriptions where all control-flow details are explicitly modeled). An open research direction in Declarative Process Mining is whether multi-perspective specifications can be supported, i.e., specifications that not only describe the process behavior from the control-flow point of view, but also from other perspectives like data or time. In this article, we address this question by considering SAT (Propositional Satisfiability Problem) as a solving technology for a number of classical problems in Declarative Process Mining, namely, log generation, conformance checking, and temporal query checking. To do so, we first express each problem as a suitable FO (First-Order) theory whose bounded models represent solutions to the problem, and then find a bounded model of such theory by compilation into SAT.

Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecasting

The explosion of digital information and the growing involvement of people in social networks led to enormous research activity to develop methods that can extract meaningful information from interaction data. Commonly, interactions are represented by edges in a network or a graph, which implicitly assumes that the interactions are pairwise and static. However, real-world interactions deviate from these assumptions: (i) interactions can be multi-way, involving more than two nodes or individuals (e.g., family relationships, protein interactions), and (ii) interactions can change over a period of time (e.g., change of opinions and friendship status). While pairwise interactions have been studied in a dynamic network setting and multi-way interactions have been studied using hypergraphs in static networks, there exists no method, at present, that can predict multi-way interactions or hyperedges in dynamic settings. Existing related methods cannot answer temporal queries like what type of interaction will occur next and when it will occur. This paper proposes a temporal point process model for hyperedge prediction to address these problems. Our proposed model uses dynamic representation learning techniques for nodes in a neural point process framework to forecast hyperedges. We present several experimental results and set benchmark results. As far as our knowledge, this is the first work that uses the temporal point process to forecast hyperedges in dynamic networks.

Theory and Practice of Relational-to-RDF Temporal Data Exchange and Query Answering

We consider the problem of answering temporal queries on RDF stores, in presence of atemporal RDFS domain ontologies, of relational data sources that include temporal information, and of rules that map the domain information in the source schemas into the target ontology. Our proposed practice-oriented solution consists of two rule-based domain-independent algorithms. The first algorithm materializes target RDF data via a version of data exchange that enriches both the data and the ontology with temporal information from the relational sources. The second algorithm accepts as inputs temporal queries expressed in terms of the domain ontology using a lightweight temporal extension of SPARQL, and ensures successful evaluation of the queries on the materialized temporally-enriched RDF data. To study the quality of the information generated by the algorithms, we develop a general framework that formalizes the relational-to-RDF temporal data-exchange problem. The framework includes a chase formalism and a formal solution for the problem of answering temporal queries in the context of relational-to-RDF temporal data exchange. In this article, we present the algorithms and the formal framework that proves correctness of the information output by the algorithms, and also report on the algorithm implementation and experimental results for two application domains.

TransVOD: End-to-End Video Object Detection With Spatial-Temporal Transformers.

Detection Transformer (DETR) and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous complex hand-crafted detectors. However, their performance on Video Object Detection (VOD) has not been well explored. In this paper, we present TransVOD, the first end-to-end video object detection system based on simple yet effective spatial-temporal Transformer architectures. The first goal of this paper is to streamline the pipeline of current VOD, effectively removing the need for many hand-crafted components for feature aggregation, e.g., optical flow model, relation networks. Besides, benefited from the object query design in DETR, our method does not need post-processing methods such as Seq-NMS. In particular, we present a temporal Transformer to aggregate both the spatial object queries and the feature memories of each frame. Our temporal transformer consists of two components: Temporal Query Encoder (TQE) to fuse object queries, and Temporal Deformable Transformer Decoder (TDTD) to obtain current frame detection results. These designs boost the strong baseline deformable DETR by a significant margin (3 %-4 % mAP) on the ImageNet VID dataset. TransVOD yields comparable performances on the benchmark of ImageNet VID. Then, we present two improved versions of TransVOD including TransVOD++ and TransVOD Lite. The former fuses object-level information into object query via dynamic convolution while the latter models the entire video clips as the output to speed up the inference time. We give detailed analysis of all three models in the experiment part. In particular, our proposed TransVOD++ sets a new state-of-the-art record in terms of accuracy on ImageNet VID with 90.0 % mAP. Our proposed TransVOD Lite also achieves the best speed and accuracy trade-off with 83.7 % mAP while running at around 30 FPS on a single V100 GPU device. Code and models are available at https://github.com/SJTU-LuHe/TransVOD.

Quickening Data-Aware Conformance Checking through Temporal Algebras

A temporal model describes processes as a sequence of observable events characterised by distinguishable actions in time. Conformance checking allows these models to determine whether any sequence of temporally ordered and fully-observable events complies with their prescriptions. The latter aspect leads to Explainable and Trustworthy AI, as we can immediately assess the flaws in the recorded behaviours while suggesting any possible way to amend the wrongdoings. Recent findings on conformance checking and temporal learning lead to an interest in temporal models beyond the usual business process management community, thus including other domain areas such as Cyber Security, Industry 4.0, and e-Health. As current technologies for accessing this are purely formal and not ready for the real world returning large data volumes, the need to improve existing conformance checking and temporal model mining algorithms to make Explainable and Trustworthy AI more efficient and competitive is increasingly pressing. To effectively meet such demands, this paper offers KnoBAB, a novel business process management system for efficient Conformance Checking computations performed on top of a customised relational model. This architecture was implemented from scratch after following common practices in the design of relational database management systems. After defining our proposed temporal algebra for temporal queries (xtLTLf), we show that this can express existing temporal languages over finite and non-empty traces such as LTLf. This paper also proposes a parallelisation strategy for such queries, thus reducing conformance checking into an embarrassingly parallel problem leading to super-linear speed up. This paper also presents how a single xtLTLf operator (or even entire sub-expressions) might be efficiently implemented via different algorithms, thus paving the way to future algorithmic improvements. Finally, our benchmarks highlight that our proposed implementation of xtLTLf (KnoBAB) outperforms state-of-the-art conformance checking software running on LTLf logic.

A pattern accumulated compression method for trajectories constrained by urban road networks

In an edge computing framework, a moving object in an urban road network can collect both its own and others’ trajectories to answer spatial–temporal queries. How to store the ever increasing amount of trajectories with limited storage space is a great challenge. We observe that the traces of moving objects in urban road networks usually exhibit great repetition in terms of spatial and velocity distributions. So we propose two strategies, Pattern Accumulated Compression based on Average Velocity (PAC-AV) and Pattern Accumulated Compression based on Representative Velocity (PAC-RV), to compress trajectories online on the mobile devices. The former mainly exploits the path pattern to compress trajectories while the latter exploits both the path pattern and the velocity pattern to compress trajectories. Both PAC-AV and PAC-RV compress the trajectories by two stages. First, we extract the spatial as well as velocity components according to a velocity deviation threshold. Second, we compress the components into a series of identifiers leveraging a Path Pattern Dictionary (PPD) and a Velocity Pattern Dictionaries (VPD). Our method not only greatly cuts down the storage space but also directly supports the velocity-related queries on the compressed trajectories. Experimental results demonstrate the effectiveness and efficiency of our method.

Replicated Versioned Data Structures for Wide-Area Distributed Systems

In this work, we investigate the integration of replicated versioned data structures and append-only distributed storage systems. Doing so facilitates high availability and scalability while providing developer access to different versions of program data structures across program executions. Modern distributed systems such as the Internet of Things (IoT) often employ multi-tiered (cloud/edge/sensors) architectures consisting of a wide array of heterogeneous devices generating data frequently. Hence system availability is imperative to avoid data loss, while scalability is required for the efficient operation of the system not only within the same tier but across different tiers as well. Our proposed approach replicates, persists, and versions <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">program data structures</i> such as binary search trees and linked lists for use in distributed IoT applications. The versioning and persistence of these structures aid failure recovery and facilitate system debugging from its inception instead of making such considerations an afterthought. Moreover, our experiments suggest versioned data structures can perform better in applications performing high volumes of temporal queries versus traditional methods of persisting data (e.g., in a database). We empirically evaluate the overheads associated with versioning and storage persistence of program data structures, present experimental results for multiple end-to-end applications, and demonstrate the scalability of this approach.

Ontology-Based News Linking for Semantic Temporal Queries

Daily newspapers publish a tremendous amount of information disseminated through the Internet. Freely available and easily accessible large online repositories are not indexed and are in an un-processable format. The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed. There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora, especially for South Asian languages. The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language. Lack of indexing, primarily semantic indexing of the daily news items, makes news items impracticable for any querying. Moreover, the most straightforward search facility does not support these unindexed news resources. Our study addresses this gap by associating and marking the newspaper images with one of the widely spoken but under-resourced languages, i.e., Urdu. The present work proposed a method to build a benchmark corpus of news in image form by introducing a web crawler. The corpus is then semantically linked and annotated with daily news items. Two techniques are proposed for image annotation, free annotation and fixed cross examination annotation. The second technique got higher accuracy. Build news ontology in protégé using Ontology Web Language (OWL) language and indexed the annotations under it. The application is also built and linked with protégé so that the readers and journalists have an interface to query the news items directly. Similarly, news items linked together will provide complete coverage and bring together different opinions at a single location for readers to do the analysis themselves.

HyperBit: A temporal graph store for fast answering queries

Relationships or interactions among entities interactions often have occurrence time. So, temporal graph is becoming a popular model to represent temporal data. Temporal graph is generally much larger than corresponding non-temporal graph because an non-temporal edge may have many corresponding temporal edges. It raises challenges for querying temporal graphs. Here, we present HyperBit, a temporal graph store which can answer temporal queries efficiently. HyperBit models temporal labeled graph as a series of updates or log records on graph. Then we design an efficient partition storage for log. Since it is costly to answer temporal queries using logs due to full scan of log, we propose an optimal algorithm to build some snapshots to speedup query processing. So, HyperBit can answer temporal queries by applying log records on the snapshot close to the time in query. HyperBit employs SPARQL instead of a new language to describe temporal queries. Thus, HyperBit can process non-temporal queries on temporal/non-temporal graphs. Extensive experiments show that HyperBit significantly outperforms RDF-3x, Jena-TDB in terms of update speed while it has a compact storage. When querying static graphs, HyperBit also outperforms RDF-3X, Jena-TDB by a wide margin and is on par with TripleBit. For temporal queries, HyperBit can easily handle billion graphs, maintaining linear time growth so that has excellent scalability.

A Novel Approach to Improve the Performance of the Database Storing Big Data with Time Information

Big data is defined as data sets that are too large and/or complex to be processed by classical data processing methods. Big data analysis is essential because it enables more competent business movements, more efficient operations, and higher profits by using the data of institutions and organizations. However, large datasets are difficult to analyze because they are produced quickly, require large storage areas in computer systems, and the diversity of their data. In this study, a new approach using the denormalization method is proposed to accelerate the response time of the database in database systems where large volumes of data containing historical information are stored. Denormalization is defined as the process of adding rows or columns that are not needed to increase the reading performance of the database to the database system that has been normalized. In the proposed approach in this study, a large-volume dataset consisting of real spatial data belonging to Kayseri Metropolitan Municipality, containing temporal information and having approximately 96,000,000 row records, was used. In the proposed approach, the response time of the query is accelerated by recording the time information as numbers to increase the query performance of large volumes of data recorded in date format due to the temporal query process. The performance of the proposed method is compared with the performance of the normalization method using actual data on Microsoft SQL Server and Oracle database systems. The method proposed in the experimental evaluations shows that it works approximately eight times faster. In addition, the experimental results showed that the proposed method improves query performance more than the normalization-based method as the data size increases.

A Temporal JSON Data Model and Its Query Languages

As a ubiquitous form of data in the natural world, time has been widely used in many domains such as the military, finance, medical treatment, and environment. Temporal data models that are used to model the dynamic development process of data are constantly proposed, such as temporal database and temporal XML. With the rapid development and popularization of the Web, the requirement of dealing with diverse data efficiently is becoming more important. As a new generation of data exchange language, JSON (JavaScript Object Notation) has been widely used because it is lightweight, has fast parsing, and has high transmission efficiency. In this paper, we propose a novel temporal JSON data model. Based on the proposed temporal JSON data model, we further propose two temporal JSON query languages, t-JSONPath, and t-JSONiq, which are the temporal extensions to JSONPath and JSONiq, respectively. With the proposed temporal JSON data model, we demonstrate our temporal JSON query languages with typical temporal query examples and evaluate their query response times.

MVLevelDB: Using Log-Structured Tree to Support Temporal Queries in IoT

Although LSM-trees are commonly adopted in many NoSQLs as they can significantly improve the write performance in updating a database, most of the proposed LSM-trees are concentrated on storing single version of data. On the other hand, in many IoT applications, it is important to maintain the old versions of data in addition to the latest version. In this paper, we introduce our design and implementation of an enhancement of LevelDB to multi-version LevelDB (called MVLevelDB) with the purpose to efficiently support temporal queries on multi-version data in IoT applications. Based on the temporal consistency, we formulated the Log-structured Multi-version tree (LSMV-tree) to be implemented into MVLevelDB. In LSMV-tree, each data version is associated with two time-stamps to define its validity interval, and both the data versions and the components are time-sorted to improve the efficiency in searching data in processing temporal queries. To handle the problem of multi-components data versions, we designed the Data version Duplication (DvD) method in which a data version will be duplicated in the next component if it is valid while its component is being flushed from the main memory to disk storage. Extensive experiments using a benchmark program have been performed to investigate the performance of MVLevelDB as compared with LevelDB both in writing and reading data.

Design and implementation of a 4D cadastral legal model for Turkish land administration infrastructure based on LADM

This article discusses 3D cadastral objects registration with time attributes and implementation within the Turkish Land Administration Domain Model (LADM) framework. This research will discuss the practical pathway towards realizing LADM-based 3D cadastral objects registration and focuses on extending the representation of those cadastral objects to 4D (3D + t) with time attributes in alignment with the jurisdictional settings in Turkey. The prototype development begins with data modelling. Also, the data was managed in an open-source PostgreSQL database. The developed prototype can answer all questions about cadastral objects, such as property, right, restrictions and responsibility (RRR) types, temporal queries and spatial source document and usage type of cadastral objects. It has been anticipated that this study may serve as a model for other national authorities with similar land management systems, particularly cadastral objects with time-qualification records, including the existing Spatial Data Infrastructure (SDI) initiative in Turkey.

Oral corticosteroid use and the risk of developing avascular necrosis: A large retrospective review.

The risk of adverse events, specifically avascular necrosis (AVN), associated with corticosteroid use is not well reported. The aim of this study was to evaluate the prevalence of AVN among patients with prior oral corticosteroid administration. An institutional database query recognized 113,734 adult patients with oral corticosteroid administration between January 2006 and May 2017. A temporal query performed on this cohort determined that 789 had a diagnosis of AVN following oral corticosteroids. A retrospective review was performed on this cohort. Data collected included demographics, comorbidities, date of initial oral corticosteroid exposure, and time to diagnosis of AVN. Records without radiographic confirmation of AVN were excluded from analysis. Patients with cumulative lifetime dosages greater than 10,000mg prednisone were excluded from analysis. A total of 789 patients with oral corticosteroid use prior to diagnosis of AVN were identified. Five hundred and seventy-two patients were excluded due to insufficient documentation of oral corticosteroid dosage, no radiographic evidence supporting the diagnosis of AVN, insufficient data confirming the temporal relationship between oral corticosteroids and AVN, and/or a cumulative dosing of>10,000mg prednisone. This left 217 patients included in the analysis. The mean duration of use prior to diagnosis of AVN was 219(±374) days, and mean cumulative dose was 3314(±2908)mg prednisone equivalents. Mean time between diagnosis of AVN and onset of pathologic fracture was 379(±1046)days. For patients receiving low cumulative doses of oral corticosteroids, corticosteroids pose a small risk of development of AVN. More studies are required to better characterize risk.

Timo: In‐memory temporal query processing for big temporal data

AbstractToday's internet applications generate massive temporal data anywhere and anytime. Although some disk‐based temporal systems are currently available, they suffer from poor I/O performance, especially when applied in intelligent applications deployed in the cloud and edge environments. Therefore, how to process temporal operations with low latency and high throughput becomes a crucial problem for efficient data processing. This paper proposes Timo, a distributed in‐memory temporal query and analytic model for big temporal data. Firstly, a space‐efficient temporal index is proposed to support more efficiently query performance with less memory space than the state‐of‐art methods. Secondly, based on the temporal locality feature of temporal queries, Timo proposes a partitioner mechanism, which utilizes forward scan algorithm, to improve query throughput. Thirdly, some optimal strategies are proposed to improve the execution process of temporal query and analysis, which reduce intermediate result data size and improve the throughput much further. Lastly, we implement the Timo system on the Apache Spark platform, which extends Spark dataset API with temporal query and analysis API functions for users. Extensive experimental results show that the Timo system outperforms other Spark‐based temporal systems in terms of both query latency and throughput.